org.apache.mahout.classifier.bayes.mapreduce.cbayes.CBayesThetaNormalizerDriver.java Source code

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/**
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package org.apache.mahout.classifier.bayes.mapreduce.cbayes;

import java.io.IOException;
import java.util.Map;

import org.apache.hadoop.conf.Configurable;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.DefaultStringifier;
import org.apache.hadoop.io.DoubleWritable;
import org.apache.hadoop.mapred.FileInputFormat;
import org.apache.hadoop.mapred.FileOutputFormat;
import org.apache.hadoop.mapred.JobClient;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.SequenceFileInputFormat;
import org.apache.hadoop.mapred.SequenceFileOutputFormat;
import org.apache.hadoop.util.GenericsUtil;
import org.apache.mahout.classifier.bayes.common.BayesParameters;
import org.apache.mahout.classifier.bayes.io.SequenceFileModelReader;
import org.apache.mahout.classifier.bayes.mapreduce.common.BayesJob;
import org.apache.mahout.common.HadoopUtil;
import org.apache.mahout.common.StringTuple;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

/** Create and run the Bayes Trainer. */
public class CBayesThetaNormalizerDriver implements BayesJob {

    private static final Logger log = LoggerFactory.getLogger(CBayesThetaNormalizerDriver.class);

    @Override
    public void runJob(Path input, Path output, BayesParameters params) throws IOException {
        Configurable client = new JobClient();
        JobConf conf = new JobConf(CBayesThetaNormalizerDriver.class);
        conf.setJobName("Complementary Bayes Theta Normalizer Driver running over input: " + input);

        conf.setOutputKeyClass(StringTuple.class);
        conf.setOutputValueClass(DoubleWritable.class);
        FileInputFormat.addInputPath(conf, new Path(output, "trainer-weights/Sigma_j"));
        FileInputFormat.addInputPath(conf, new Path(output, "trainer-tfIdf/trainer-tfIdf"));
        Path outPath = new Path(output, "trainer-thetaNormalizer");
        FileOutputFormat.setOutputPath(conf, outPath);
        // conf.setNumMapTasks(100);
        // conf.setNumReduceTasks(1);
        conf.setMapperClass(CBayesThetaNormalizerMapper.class);
        conf.setInputFormat(SequenceFileInputFormat.class);
        conf.setCombinerClass(CBayesThetaNormalizerReducer.class);
        conf.setReducerClass(CBayesThetaNormalizerReducer.class);
        conf.setOutputFormat(SequenceFileOutputFormat.class);
        conf.set("io.serializations",
                "org.apache.hadoop.io.serializer.JavaSerialization,org.apache.hadoop.io.serializer.WritableSerialization");
        // Dont ever forget this. People should keep track of how hadoop conf
        // parameters and make or break a piece of code

        FileSystem dfs = FileSystem.get(outPath.toUri(), conf);
        HadoopUtil.overwriteOutput(outPath);

        Path sigmaKFiles = new Path(output, "trainer-weights/Sigma_k/*");
        Map<String, Double> labelWeightSum = SequenceFileModelReader.readLabelSums(dfs, sigmaKFiles, conf);
        DefaultStringifier<Map<String, Double>> mapStringifier = new DefaultStringifier<Map<String, Double>>(conf,
                GenericsUtil.getClass(labelWeightSum));
        String labelWeightSumString = mapStringifier.toString(labelWeightSum);

        log.info("Sigma_k for Each Label");
        Map<String, Double> c = mapStringifier.fromString(labelWeightSumString);
        log.info("{}", c);
        conf.set("cnaivebayes.sigma_k", labelWeightSumString);

        Path sigmaKSigmaJFile = new Path(output, "trainer-weights/Sigma_kSigma_j/*");
        double sigmaJSigmaK = SequenceFileModelReader.readSigmaJSigmaK(dfs, sigmaKSigmaJFile, conf);
        DefaultStringifier<Double> stringifier = new DefaultStringifier<Double>(conf, Double.class);
        String sigmaJSigmaKString = stringifier.toString(sigmaJSigmaK);

        log.info("Sigma_kSigma_j for each Label and for each Features");
        double retSigmaJSigmaK = stringifier.fromString(sigmaJSigmaKString);
        log.info("{}", retSigmaJSigmaK);
        conf.set("cnaivebayes.sigma_jSigma_k", sigmaJSigmaKString);

        Path vocabCountFile = new Path(output, "trainer-tfIdf/trainer-vocabCount/*");
        double vocabCount = SequenceFileModelReader.readVocabCount(dfs, vocabCountFile, conf);
        String vocabCountString = stringifier.toString(vocabCount);

        log.info("Vocabulary Count");
        conf.set("cnaivebayes.vocabCount", vocabCountString);
        double retvocabCount = stringifier.fromString(vocabCountString);
        log.info("{}", retvocabCount);
        conf.set("bayes.parameters", params.toString());
        conf.set("output.table", output.toString());
        client.setConf(conf);

        JobClient.runJob(conf);

    }
}